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Contract No. DE-AC36-08GO28308

A Techno-Economic Analysis 

of BEVs with Fast Charging 

Infrastructure

Preprint

Jeremy Neubauer and Ahmad Pesaran

Presented at the 27th International Electric Vehicle Symposium 

and Exhibition (EVS27)

Barcelona, Spain

November 17–20, 2013

Conference Paper

NREL/CP-5400-60007

August 2014
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EVS27

Barcelona, Spain, November 17-20, 2013

A Techno-Economic Analysis of BEVs with Fast Charging 

Infrastructure

Jeremy Neubauer1, Ahmad Pesaran2

1

National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, 

jeremy.neubauer@nrel.gov

2

National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, 

ahmad.pesaran@nrel.gov

Abstract

Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas 

emissions, but high upfront costs, battery-limited vehicle range, and concern over high battery replacement 

costs may discourage many potential purchasers. One proposed solution is to employ a subscription model 

under which a service provider assumes ownership of the battery while providing access to vast fast 

charging infrastructure. Thus, high upfront and subsequent battery replacement costs are replaced by a 

predictable monthly fee, and battery-limited range is replaced by a larger infrastructure-limited range. 

Assessing the costs and benefits of such a proposal are complicated by many factors, including customer 

drive patterns, the amount of required infrastructure, and battery life. Herein the National Renewable 

Energy Laboratory applies its Battery Ownership Model to address these challenges and compare the 

economics and utility of a BEV fast charging service plan to a traditional direct ownership option. In single 

vehicle households, where such a service is most valuable, we find that operating a BEV under a fast 

charge service plan can be more cost-effective than direct ownership of a BEV, but it is rarely more costeffective than direct ownership of a conventional vehicle. 

Keywords: Battery Ownership Model, fast charge, electric vehicles, total cost of ownership, range extension

1 Introduction

Battery electric vehicles (BEVs) offer the 

potential to reduce both oil imports and 

greenhouse gas emissions relative to 

conventional vehicles (CVs). However, in 

practice, high upfront cost, concerns over battery 

life and high battery replacement costs, and 

battery-limited vehicle range of today’s BEVs 

may discourage potential purchasers. One 

proposed solution is to employ a subscription 

model that insulates consumers from the risks of 

battery degradation and provides access to fast 

charge infrastructure. Under such a scenario, 

drivers would purchase a BEV without a battery

and pay a monthly subscription fee for access to

service-provider-owned batteries and fast charge 

infrastructure.

Comparing this option to a traditional direct 

ownership, though, is not straightforward. As 

discussed at length in [1], computing the total cost 

of ownership (TCO) of BEVs under a direct 

ownership (DO) scenario itself is challenging. 

Adding a service provider and fast charging to the 
2

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equation increases complexity via the need to 

account for fast charge infrastructure and

quantify service provider economics. 

With support from the Vehicle Technologies 

Office in the U.S. Department of Energy, the 

National Renewable Energy Laboratory has 

developed a vehicle total cost of ownership 

(TCO) calculator known as the Battery 

Ownership Model (BOM) to evaluate and 

analyze these and other challenges associated 

with the lifecycle economics of electric vehicles 

and advanced business strategies. The BOM 

accounts for vehicle and component costs, 

battery and fuel price forecasts, drive patterns, 

battery wear, charging infrastructure costs, 

purchase incentives, financing, ownership, and 

other criteria. Previously we applied this tool to 

the analysis of traditional ownership of BEVs [1] 

and plug-in hybrid electric vehicles [2] as well as 

to BEVs operated under a battery swapping 

service plan [3].

Herein we apply the BOM to compare the 

economics and utility of a BEV fast charging 

service plan option (SP-BEV) to traditional direct 

ownership of a BEV (DO-BEV) and of a CV 

(DO-CV). After briefly discussing our general 

approach to modelling TCO and computing the 

DO-CV and DO-BEV economics, we evaluate 

the SP-BEV via the following four steps: (1) 

identifying drive patterns best suited to this plan, 

(2) modelling service usage statistics for the 

selected drive patterns, (3) calculating the cost of 

different service plan options given these 

statistics, and (4) evaluating the economics of 

individual drivers under realistically priced 

service plans.

2 Total Cost of Ownership 

Calculation Approach

The methods and assumptions applied herein for 

computation of TCO are generally consistent 

with those in [1, 3] except when explicitly noted 

otherwise. The vehicle economics considered 

include vehicle and related infrastructure 

purchases, financing, fuel (gasoline and 

electricity) costs, non-fuel operating and 

maintenance costs, battery replacement, salvage 

value, and costs associated with a service 

provider when applicable. Battery degradation, 

charging strategies, and drive patterns play an 

important role in each of these elements as 

discussed in [1-3]. We use 398 real world 

longitudinal drive patterns from the Puget Sound 

Regional Council’s Travel Choice Study (TCS) [4]

to calculate vehicle usage and create battery duty 

cycles. The vehicles employed for this study are 

the same as those in [1, 3], where a variable 

drivetrain is adapted to a standard mid-size sedan 

platform to yield a 9 second 0–60 mph acceleration 

time and a specified range (Table 1). Both fuel and 

electricity consumption are calculated via 

simulation of the highway and urban driving 

dynamometer schedule weighted and combined to 

be representative of the U.S. Environmental 

Protection Agency window sticker rating [5] 

assuming a constant 300 W auxiliary load for 

accessories [6].

3 Direct Ownership Analysis

Prior to addressing the fast charge service provider 

cases, we analyze two traditional competing 

alternatives: DO-BEV and DO-CV. The TCO for 

each vehicle is computed over all 398 TCS drive 

patterns assuming a 2015 vehicle purchase year, 15 

year analysis period, and 8% driver discount rate

per the methods and assumptions in [3]. The CV 

analysis assumes the efficiency reported in Table 1 

and the national average gasoline price forecasts as 

reported in the Energy Information 

Administration’s (EIA’s) 2011 high oil price 

scenario [7] (Figure 1). No range restrictions are 

placed on CV travel.

For the BEV analysis, we employ the Table 1 

vehicle specifications for the 75-mile-range BEV 

utilizing a 100% maximum state of charge (SOC), 

as this was identified as the most cost-effective 

BEV solution when the cost of unachievable 

vehicle miles travelled (VMT) is high (e.g., single 

vehicle households) in [1] and is a reasonable 

representation of currently available BEV options.

We evaluate three different battery manufacturing 

costs ($125/kWh, $300/kWh, and $475/kWh) that 

span the DOE’s advanced battery cost targets [8] 

and several industry battery cost forecasts [9-11]. 

A 1.5 manufacturing-to-retail mark-up [12-14] is 

applied to calculate the price offered to the 

consumer or service provider. We assume no tax 

credits or other purchase incentives are available.

A 32 amp, 240 V charger is assumed installed at 

the driver’s home for $1,200. Residential customer 

electricity price projections from the EIA’s 2011 

baseline scenario [7] (Figure 2) are employed. The 

amount of energy consumed is calculated based 

upon an 85% charging efficiency and the achieved 

annual VMT, which changes annually as a 

function of drive pattern and battery degradation. 
3

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Figure 1. Employed gasoline prices (2012 dollars)

Figure 2. Employed residential electricity prices (2012 

dollars)

Battery degradation is calculated using a highfidelity life model [15] that projects capacity loss 

and resistance growth at the end of each service 

year based on the selected drive pattern, a just-intime charge strategy, and national average 

environmental conditions. Minimum SOC is 

adjusted each year such that no less than 80% of

beginning of life (BOL) power can be delivered at 

the end of charge depleting operation due to 

minimum voltage requirements, thus translating 

the effect of power fade to a reduction in available 

energy, and thereby vehicle range. In addition to 

calculating achieved VMT, we also leverage this 

capability to employ bounded, cost-optimal battery 

replacement schedules as originally proposed and 

examined in [1]. 

A cost is applied to unachievable VMT per the 

high-cost approach described in [1], which 

assumes that a CV is rented via a car-share 

program on days where the daily VMT exceeds the 

range capability of the car. This is selected over 

the low cost approach (representative of a multicar household with an additional CV available for 

long trips) to better represent likely candidates for 

a fast charge service plan—those without an 

additional means of convenient, long-range 

transportation available. 

We compare the TCO of the DO-BEV to that of 

the DO-CV with the aforementioned conditions for

all 398 drive patterns in Figure 3. Under the low 

battery cost scenario, 54% of simulated drive 

patterns achieve a DO-BEV TCO within 20% of 

that of a DO-CV, while the remaining 46% of 

simulated drive patterns incur a TCO premium of 

20% or greater when electing to drive a DO-BEV 

rather than a DO-CV. With the medium and high 

battery costs, a much larger percentage of 

simulated drive patterns incur a TCO premium of 

20% or more when electing the DO-BEV.

$4.00 

$5.00 

$6.00 

2015 2020 2025 2030

Gasoline Prices 

($/gal)

Year

$0.110 

$0.115 

$0.120 

2015 2020 2025 2030

Residential 

Electricity Price 

($/kWh)

Year

Table 1. Vehicle specifications (all prices in 2012 dollars)

Vehicle Electric 

Range (mi)

Maximum 

SOC

Engine or 

Motor Power 

(kW)

Battery 

Energy (kWh)

Vehicle 

Efficiency 

(kWh/mi)

2015 Vehicle 

Retail Price 

(w/o Battery)

CV 0 n/a 100 0 32 mi/gal $17,687

BEV50 50

100% 79.7 16.6 0.332 $16,150

95% 80.3 17.5 0.333 $16,161

90% 80.8 18.6 0.334 $16,170

BEV75 75

100% 85.3 25.7 0.343 $16,252

95% 86.3 27.2 0.345 $16,268

90% 87.2 28.8 0.347 $16,285

BEV100 100

100% 91.1 35.4 0.355 $16,356

95% 93.0 37.6 0.358 $16,390

90% 94.4 40.0 0.361 $16,415
4

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A significant factor in calculating these trends is 

the assumed high cost of unachievable VMT.

Similar comparisons under a low cost of 

unachievable VMT show that a DO-BEV is more 

broadly cost-effective [1]. However, this low cost 

of unachievable VMT assumption is not as 

applicable to single-vehicle households interested 

in BEVs. Reducing the amount and cost of 

unachievable VMT is thus the motivation for 

investigating the fast charge SP-BEV.

Figure 3. DO-BEV to DO-CV cost ratio distributions

4 Fast Charge Service Plan 

Analysis

In this section we assess the TCO to the 

consumer of BEVs operated under a fast charge 

service plan. Service plan fees are calculated 

using a bottom-up approach that accounts for all 

of the service provider’s battery, infrastructure, 

electricity, and other costs, as well as the cost of 

financing such an operation. There are four 

phases to this analysis:

(1) Analyzing all 398 drive patterns to downselect a subset of drive patterns suitable for 

more detailed fast charge analysis

(2) Identifying average service usage statistics 

for this subset of drive patterns, including 

battery life, electricity usage, and fast charge 

frequency

(3) Calculating infrastructure requirements and 

service provider fees for multiple scenarios 

based on the identified service usage statistics 

and a rigorous economic model of the service 

provider’s business

(4) Investigating individual driver economics 

under the calculated service provider fees.

4.1 Identifying Drive Patterns Suitable 

to Fast Charge Service Plan BEVs

In [3] we identified drive patterns most suitable to 

a BEV with a range extension service plan when 

the range extension features are perfect and 

without limit. The process down-selected drive 

patterns that showed the most potential for 

economic savings over direct ownership of either a 

CV or a BEV, on the basis that financial advantage 

over both direct ownership options is an important 

criterion for a consumer to consider when electing 

to subscribe to such a service plan. The defining 

characteristic of drive patterns in the resultant 

subset is annual mileage. The minimum annual 

VMT of the selected subset is 11,471 miles, and 

every drive pattern with an annual VMT greater 

than 16,446 miles from the full TCS data set is 

included (10% of the entire TCS data set, but 40% 

of the 100 selected drive patterns). This is logical 

given that our selected drive patterns are parsed on 

an economic basis, where the primary means of 

financial benefit is reducing the cost of gas 

expenditures (proportional to VMT), and the 

limitations of achievable mileage have been 

eliminated by the assumption of a perfect range 

extension technology. The distribution of annual 

VMT of this selection is shown in Figure 4.

0

0.1

0.2

0.3

0.4

0.5

0.8 to 11 to 1.2

1.2 to 1.41.4 to 1.61.6 to 1.8

1.8 to 22 to 2.2

2.2 to 2.42.4 to 2.62.6 to 2.8

Direct Ownership BEV to Direct Ownership CV 

Cost Ratio

$125/kWh Batteries

0

0.1

0.2

0.3

0.4

0.5

0.8 to 11 to 1.2

1.2 to 1.41.4 to 1.61.6 to 1.8

1.8 to 22 to 2.2

2.2 to 2.42.4 to 2.62.6 to 2.8

Direct Ownership BEV to Direct Ownership CV 

Cost Ratio

$300/kWh Batteries

0

0.1

0.2

0.3

0.4

0.5

0.8 to 11 to 1.2

1.2 to 1.41.4 to 1.61.6 to 1.8

1.8 to 22 to 2.2

2.2 to 2.42.4 to 2.62.6 to 2.8

DO-BEV to DO-CV Cost Ratio

$475/kWh Batteries

Probability Distribution Function

Figure 4. Annual VMT of selected drive patterns
5

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4.2 Service Usage Statistics

Now we analyze the service usage patterns of the 

selected 100 drive patterns under the application 

of more realistic assumptions around the VMT 

achievable via fast charge infrastructure. Relative 

to both the perfect range extension and direct 

ownership cases, our improved set of 

assumptions for BEVs operated under a fast 

charging service plan affects three factors that 

must be accounted for to model service usage 

statistics: achievable VMT, battery degradation, 

and battery replacement criteria. 

Achievable VMT is affected primarily by vehicle 

range and range extension infrastructure. We 

consider three vehicle ranges (50, 75, and 100 

miles) and three maximum SOCs (90%, 95%, 

and 100%) with the vehicle properties defined in 

Table 1. The combination of vehicle range with 

time constraints to complete a day’s driving and 

charging, as well as with the perceived 

inconvenience of fast charge stops, will limit 

daily VMT. Herein we account for the impact of

time limitations by allocating a maximum of 24 

hours to a day’s driving and charging activities 

(allowing successive back-to-back occurrences of 

the most demanding drive days). We also restrict 

the number of daily fast charges to a maximum 

of two. When more than two fast charges would 

be required by a day’s driving, or when the time 

required to complete the driving, charging, and 

range extensions exceeds 24 hours, we assume 

that the driver instead acquires a vehicle from a 

car-share program. Note that car-share cost 

accounting is performed as detailed in [1] and is 

not a cost covered by the service provider per our 

assumptions, although in practice this could be 

something a service provider may choose to offer 

with its BEV services. 

Battery degradation is affected by the different 

cycle and SOC history induced by the utilization 

of fast charge infrastructure, and thus we modify 

our input to the battery degradation algorithm to 

accurately account for these effects under the fast 

charge service plan. However, the increased 

charge rate of these cycles also affects battery 

wear, but unfortunately the battery degradation 

model we employ does not internally account for 

additional wear mechanisms induced by highrate charging. Although an argument might be 

made that the incremental wear of such high-rate 

charging is minimal on the grounds that (1) the 

time at peak charge rate of approximately 2C is 

limited to much less than 30 minutes due to 

voltage related limitations [16], and (2) early 

results from ongoing testing have shown no 

discernible effect of increased charge rate on 

degradation [23], we instead adjust results of the 

life model to exaggerate wear from fast charge 

cycle when desired. We do so by adding a userdefined fast charge wear factor that amplifies the 

impact of fast charge cycles’ effect on capacity and 

resistance degradation. As implemented, a fast 

charge wear factor of 10 scales a model-predicted 

single-cycle capacity loss of 0.1% to 1.0% to 

account for the effect of increased rate. For this 

study we shall employ fast charge wear factors of 

1 and 10 to estimate possible effects of fast 

charging on battery wear and to investigate the 

economic sensitivity thereof.

Note that home charging is unaffected: the fast 

charge wear factor is not applied to these charge 

cycles. As before we continue to assume that home 

charging is performed on a just-in-time schedule, 

such that the maximum SOC is reached just prior 

to the vehicle’s first departure of the day. 

It is also necessary to adjust the battery 

replacement criteria. In the direct-ownership case, 

we applied a cost-optimal replacement schedule. 

As discovered in [1], this approach results in long 

battery lifetimes and significantly reduced vehicle 

range near end of life (EOL). Although this may be 

financially optimal for individual drivers, higher 

certainty and consistency of battery performance is 

an attractive consumer benefit of a service plan. 

Further, guaranteed performance eliminates 

inhibitions to using fast charging infrastructure for 

fear of excess battery degradation. Accordingly, 

we enforce a battery EOL condition of 80% BOL 

range at 80% BOL power. Once this level of 

performance is breached, the service provider is 

assumed to replace the customer’s battery with a 

new one to meet consumer range expectations.

The resultant average battery life, number of 

annual battery swaps, annual electricity 

consumption, and achieved utility factor are 

reported in Table 2 for every combination of 

vehicle range, max SOC, and fast charge wear 

factor. As might be expected, we find that the 

number of average annual fast charge events is 

reduced by increased vehicle range, promoting 

average battery life increases with increased 

vehicle range. Increasing vehicle range from 50 to 

75 miles improves life due to decreased cycling. 

Increasing vehicle range from 75 to 100 miles, 

however, decreases battery life slightly due to 

higher average SOC through life. Similarly, max 

SOC has a strong effect on battery life. Finally, 

fast charge wear factor is shown to reduce battery 
6

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lifetime noticeably for all BEV50 scenarios, due 

to the much increased fast charge frequency, but 

is seen to have little to no effect on the BEV75 

and BEV100 cases. 

Table 2 also shows the utility factor for each 

scenario, defined as the ratio of miles travelled 

electrically in a BEV to the total VMT of the 

original drive pattern. Recall that we impose a 

maximum of two fast charges per day, which 

reduces the frequency at which the BEV is 

utilized. Further, the time required for driving

and charging also affects BEV utilization. For the 

cases studied herein, we find that vehicle range is 

the primary determinant of the utility factor, 

yielding 77%, 83%, and 86% utility factors for 

the 50, 75, and 100 mile BEVs. For comparison, 

the same high mileage drive cycles averaged a 

51% utility factor in the DO-BEV case without 

fast charge. Clearly, all service plan cases are 

capable of significantly increasing utility factors. 

The fact that these values are not closer to 100%, 

though, implies that an alternative mode of 

transportation will still be necessary for some 

drivers on some occasions, which will impact 

individual driver economics.

4.3 Service Provider Fee

Now we apply the usage statistics for likely 

subscribers acquired in Section 4.2 to calculate 

expected monthly service provider fees under 

various scenarios. To do so, we construct a 

financial model of a service provider that includes 

all capital and recurring costs, return on equity 

(ROE), cost of debt, and other factors as described 

herein, then calculate the monthly fee charged to 

customers that achieves ROE requirements at the 

end of 15 years of operation. 

4.3.1 Financial Accounting 

The business of the service provider is very capital 

intensive due primarily to the capital cost of 

battery packs and charging infrastructure. Thus, 

Table 2. Calculated fast charge service plan usage statistics

Range Max SOC Fast Charge 

Wear Factor

Battery Life

(yrs)

Annual Electricity 

(kWh)

Fast Charge Events 

per Year

(No.)

Utility Factor

50 mi

100%

1 9.0 4952 135.1 76%

10 7.9 4943 135.1 76%

95%

1 11.7 5015 131.2 77%

10 10.2 5009 128.8 77%

90%

1 14.4 5057 126.9 77%

10 13.0 5079 125.9 78%

75 mi

100%

1 9.8 5480 59.3 83%

10 9.8 5479 59.3 83%

95%

1 13.0 5519 58.0 83%

10 12.9 5520 58.0 83%

90%

1 14.7 5570 53.0 83%

10 14.7 5570 53.0 83%

100 mi

100%

1 9.0 5827 29.4 86%

10 9.0 5827 29.4 86%

95%

1 12.1 5898 28.7 86%

10 12.1 5898 28.7 86%

90%

1 14.5 5954 26.8 86%

10 14.5 5954 26.8 86%
7

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the means of financing the business is expected 

to have a considerable impact on the required 

service provider fee. In this analysis, we assume 

that the service provider acquires the necessary 

capital to finance the battery and infrastructure 

purchases of the first year of operation equally 

from debt and equity investments at the 

beginning of that year. The cost of these 

expenditures is determined as discussed below 

assuming an initial customer base of 10,000 

subscribers. The cost of debt is varied from 4%

to 12%, with annual debt payments calculated to 

pay off the full balance in 15 years where interest 

accrues annually. Our assumed ROE, ranging 

from 5% to 15%, is coupled to the cost of debt, 

and is applied as shall be discussed subsequently.

Annual revenue is calculated from the monthly 

service provider fee and the number of 

customers. From this the gross taxable income is 

computed after deducting annual operating 

expenses (described below), interest payments on 

debt, depreciation of charging infrastructure 

(5%/yr) and batteries (annualized per the 

calculated average battery life of Table 2), and 

any applicable loss carry forwards from previous 

years. Taxes are then computed against the gross 

taxable income assuming an average 39.3% 

corporate tax rate [17].

The remaining working capital at the end of each 

year is calculated by subtracting the annual debt 

payment, equity payment, operating expenses, 

and taxes from the annual revenue. We assume 

this remaining capital is spent in the subsequent 

year to buy batteries and build infrastructure to 

support additional customers. As such, the profit 

from year one determines the increase in 

customers in year two, and so on.

The monthly service fee charged is determined 

by an iterative process to ensure that the 

company net worth at year 15 is equal to the 

value of the initial equity investment after 15 

years of growth at the prescribed ROE. For 

example, if the required battery and 

infrastructure expenses for year one totals $2M 

and a 15% ROE is specified, our requirement 

would demand that the monthly service fee be set 

to result in a business with a net worth of $8.14M 

at the end of year 15—equivalent to the value of 

the initial equity investment ($1M, 50% of the 

total year-one capital requirements) growing at 

15% per year for 15 years. The net worth of the 

company at year 15 is defined simply as the sum 

of all past capital expenditures, minus the sum of 

all past depreciation taken, plus the profit made 

in the final year (note that the debt term aligns with 

the analysis term, and thus the company has no 

debt remaining at year 15 to consider). 

4.3.2 Charging and Range Extension 

Infrastructure

The total cost of home charging infrastructure is 

computed from the number of new subscribers 

each year and a flat fee of $1,200 per charge point 

installed. 

To compute the total cost of fast charge 

infrastructure, we begin by computing the fast 

charger utilization rate: the average hours per day a 

fast charge station is occupied by a customer. On 

the basis that a service plan customer might expect 

the same level of convenience and availability 

provided by today’s network of gasoline pumps, 

we first calculate today’s utilization rate for the 

average gas pump. Our calculations indicate that 

the average gas pump is occupied approximately 

1.23 hours per day [3]. Herein we shall employ an 

equivalent minimum fast charger utilization rate to 

represent a level of infrastructure availability on 

par with that of the average gas pump, and one of 

6.25 hours per day (five times the occupation of an 

average gas pump) to represent a less 

infrastructure intensive alternative. 

From here we can compute the ratio of customers 

per fast charger by dividing the utilization rate by 

the average time per day each customer spends at a 

fast charger. The average fast charge time per day 

per customer is calculated by multiplying the 

average number of annual fast charges per 

subscriber (Table 2) by the time required per event 

(30 minutes for a fast charge and 2 minutes for 

related activities; note that waiting to use the fast 

charger, if required, is not included) and dividing 

by 365 days per year. We find this ratio varies 

from a low of 6 vehicles per fast charger for the 

BEV50 with a utilization rate of 1.23 hours per 

day, to a high of 153 vehicles per fast charger for 

the BEV100 with a utilization rate of 6.25 hours 

per day. Finally, dividing the number of 

subscribers by the ratio of customers per fast 

charger yields the total number of fast chargers 

required. 

The fast charger equipment cost is determined by 

the power of the charger multiplied by a $200/kW 

cost coefficient taken from recent announcements 

of fast charger technology [18]. Charger power is 

calculated via Eq. 1 to ensure batteries are 

recharged to 80% of their available energy in a 30 

minute period. A multiplicative factor of 1.5 is 
8

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included to approximately account for the 

variable rate nature of li-ion battery charging 

induced by voltage limitations. A flat fee ranging 

from $5,000 to $50,000 to account for a broad 

range of installation costs is also included. For 

the range of vehicles we consider, this approach 

results in fast charge stations ranging from 39.8 

kW and $12,968 for the BEV50 with 100% max 

SOC and a flat fee of $5,000, to 84.6 kW and 

$67,280 for the BEV100 with 90% max SOC and 

a flat fee of $50,000.

Charge Power (kW) = 1.5*0.8* Battery Available 

Energy (kWh) / 0.5 h (1)

The total annual expenditure on fast chargers 

then becomes the incremental number of 

positions required in a given year times the 

individual position cost.

It is important to remember throughout the 

remainder of this paper that total fast charge 

infrastructure costs are a function of the number 

of customers, the fast charge utilization rate, fast 

charge flat fee, and battery available energy (as 

the charge power, and thereby power electronics 

cost, scale with available energy to keep fast 

charge time to 30 minutes).

4.3.3 Battery Expenditures 

The number of batteries purchased in a given 

year is equal to the number of new customers 

plus the number of batteries required to replace 

those removed from service due to wear. 

Batteries are removed from service based on the 

average battery lifetime per Table 2 and the 

history of new customers.

The manufacturing cost of each battery is 

computed using the size of the battery for the 

specific range and max SOC combination as 

reported in Table 1 and one of the three 

aforementioned battery costs. A manufacturingto-retail mark-up factor of 1.5 [12-14] and a sales 

tax rate of 6.2% are included to calculate the cost 

of each battery to the service provider. We then 

compute the total annual battery expenditure 

from the number of batteries purchased and the 

cost of each battery.

We credit the battery’s salvage value at the end 

of its automotive life. Salvage value is computed 

assuming it will see service in a second use 

application per the methods described in [19-20], 

assuming $18/kWh for repurposing, a used 

product discount factor of 0.75, and a health 

factor of 0.67 or 0. The value of 0.67 is based on 

our calculations of second use battery life for the 

80% range at EOL requirement per the methods 

and assumed duty cycles of [20]. Employing a 

value of 0 for the health factor effectively 

eliminates battery second use value from 

consideration. 

4.3.4 Operating Expenses

The main operating expense considered for the 

service provider is electricity. The price of 

commercial electricity per kWh shown in Figure 5 

is taken from [7] and multiplied by the amount of 

consumed electricity per subscriber (Table 2) and 

the amount of subscribers per year to yield the 

bulk cost of electricity to the service provider. 

Note that annual electricity numbers include an 

assumed efficiency of 85% for at-home charge 

points and 75% for the batteries charged on the 

higher-rate range extension infrastructure 

equipment. A fee of $20/kW/month is applied to 

the peak battery charging load at each range 

extension position to represent demand charges 

similar to those of Southern California Edison’s 

TOU-GS-3-SOP [21] and San Diego Gas and 

Electric’s AL-TOU [22] rate schedules. Total 

demand charges thereby varying from $637 to 

$1,536 per month per fast charger are added to the 

bulk cost of electricity.

We also include annual operating costs of $2,500 

for each fast charge station to cover general 

maintenance. Finally, we include the cost of 

general and administrative activities at the cost of 

$100 per subscriber per year.

Figure 5. Employed commercial electricity prices (2012 

dollars)

4.3.5 Service Plan Fee Results: Sensitivity to 

Variables

We calculate the monthly service provider fee for 

an expansive set of ROE, cost of debt, maximum 

SOC, vehicle range, battery manufacturing cost, 

fast charge wear factors, second use health factors, 

fast charger utilization rate, and fast charger flat 

$0.095 

$0.100 

$0.105 

2015 2020 2025 2030

Commercial 

Electricity Price 

($/kWh)

Year
9

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fee values as discussed above, resulting in 

thousands of unique scenarios to evaluate. To 

assess the impact of an individual variable, we 

perform a sensitivity analysis of the monthly 

service fee for each variable. For each variable, 

we calculate the median service fee of all cases 

where the variable of interest is at best-case 

(encouraging lower service provider fees) and 

worst-case (encouraging higher service provider 

fees) values, then rank the importance of that 

variable based on the difference in the two 

calculated median service fees (see Figure 6).

For example, to study the sensitivity of battery 

cost, we first calculate the median of all service 

fees where the battery cost is set to $125/kWh 

(yielding a median service fee of $207/month). 

We repeat this process when battery cost is set to 

$475/kWh (yielding a median service fee of 

$416/month). The large difference in median 

service fees ($209/month) indicates the high 

level of impact that battery cost has on service 

fee; indeed, in Figure 6 we see that battery cost is 

the most sensitive variable in this analysis. 

It is clear that across the range of variables 

considered in this analysis, the cost of batteries, 

the cost of financing, and the fast charger

utilization rate (which strongly impacts the total 

cost of infrastructure) are the most predominant 

factors driving the service fee value. At the 

opposite end of the spectrum, we find that the 

maximum SOC, the fast charger flat fee, and the

fast charge wear factor have negligible impact on 

the service fee, the latter due to the low

frequency of fast charge events for most drivers. 

4.3.6 Service Provider Fee Results: Cost 

Breakdown 

Before digging deeper into the service provider 

cost results, we limit our investigation to a subset 

of variables. First, we eliminate from consideration 

multiple maximum SOCs and fast charge wear 

factors due to their small impact, proceeding only 

with a maximum SOC of 90% and a wear factor of 

10x to compose a conservative fast charging 

scenario. Second, we eliminate consideration of 

battery second use, as it also has a small impact 

and comes with considerable uncertainty. Third, 

we group the variables related to range extension 

into two classes: 

(1) A high service, high cost class where the 

utilization rate is set to the minimum and the flat 

fee is set to the maximum

(2) A low service, low cost class where the 

utilization rate is set to a maximum and the flat fee 

is set to a minimum.

Finally, we restrict the cost of financing to the 

medium case (8% cost of debt and 10% return on 

equity) on the basis that the low values are 

unlikely without government participation and that 

the high values are unlikely to support a 

compelling business case relevant to mass markets. 

The cost of batteries, the vehicle range, and the 

range extension class are thus our remaining 

variables of study. The breakdown of the monthly 

service fee for these remaining cases is shown in 

Figure 7. 

Three clearly evident points arise from these 

results. First, the service provider fees for the fast 

charge and battery swapping service plan (as 

analyzed in [3]) are not considerably dissimilar. 

Second, there is a strong difference in service fees 

between the two service plan classes. Clearly, the 

quantity and cost of infrastructure deployed—and 

thereby the level of service to the subscriber—is an 

important driver of service fee. Finally, we see that 

when battery costs are low and infrastructure cost 

is high, the cost of infrastructure is the largest 

contributor to the service fee. Thus higher vehicle 

range promotes lower service fees in such 

scenarios. In most remaining cases, though, the 

service fee is dominated by battery costs, and thus 

the service fee increases with vehicle range.

4.4 Individual Driver Economics

As noted previously, our initial assessment of 

individual driver economics (Section 4.1) assumed 

perfect range extension technology. However, 

Figure 6. Sensitivity of service provider fee to variables
10

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truly perfect range extension is not achievable 

under our fast charging assumptions (Section 

4.2). Thus, in this section we revisit the 

individual driver economics of likely subscribers 

using our assumed fast charge limitations and 

calculated service fees (Section 4.3) for 100

down-selected drive patterns (Section 4.1).

We present the results of our final TCO 

calculations in Figures 8 and 9. Figure 8 shows 

the frequency at which electing the fast charge 

service plan is more cost-effective than the DOBEV. It includes all three vehicle ranges and 

battery prices and both the low cost, low service 

and high cost, high service plan classes, 

illustrating that the fast charge service plan BEVs 

can be more economical than the DO-BEV for 

many drive patterns under a 

broad spectrum of conditions. However, Figure 9

shows that electing the service plan is more costeffective than both the DO-BEVand DO-CV 

options only when both the costs of batteries and 

infrastructure are low. 

5 Conclusions

In this study we have analyzed the economics of 

single-vehicle household (where the cost of 

unachievable travel is high) BEVs operated under 

a service plan where fast charging is provided to 

extend vehicle range. Our evaluation process 

followed four steps: (1) identifying drive patterns 

best suited to a fast charge service plan, (2) 

modelling service usage statistics for the selected 

drive patterns, (3) calculating the cost of different 

service plan options given these statistics, and (4) 

Figure 7. Fast charge service fee breakdown 

 

Figure 8. Frequency at which operating a BEV under a 

fast charge service plan is more cost effective than 

direct ownership of a BEV75

Figure 9. Frequency at which operating a BEV under a 

fast charge service plan is more cost effective than 

direct ownership of either a BEV75 or a CV
11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

evaluating the economics of individual drivers 

under realistically priced service plans. For 

comparison, we have also calculated the TCO of 

both a BEV and a CV operated under a 

conventional direct ownership scenario. A high 

fidelity battery degradation model has been 

employed throughout to forecast battery wear, its 

effect on vehicle range, and required battery 

replacements. Real world drive patterns from the 

TCS project have been utilized to support the 

calculation of realistic battery usage, the 

frequency of battery swapping events, and the 

fraction of achievable VMT. The cost of 

unachievable VMT has been accounted for based 

on the cost of popular car share programs, 

making our results most generally applicable to 

drivers without access to an alternative lower 

cost, range-unlimited mode of transportation 

(e.g., a second non-BEV car owned by or freely 

available to said driver). Further, a detailed 

accounting of the economics of a fast charge 

service provider, including consideration of the 

amount of required infrastructure, financing of 

capital expenditures, recurring costs, taxes, 

depreciation, and required ROE has been applied 

to calculate the fee charged to the consumer for 

the service plans.

As should be expected, we find that drive 

patterns with high annual VMT are generally 

best suited to fast charge service plans. The 

frequency at which subscribers used fast charge 

infrastructure varies considerably—primarily as a 

function of the range of the BEV, where shorter 

BEV ranges encourage a higher frequency of fast 

charge events. For all vehicle ranges, though, the 

utility factor is high, spanning 76% at 50 miles to 

86% at 100 miles when up to two fast charge 

events per day are allowed.

In calculating the monthly service fee, we find 

that the costs of batteries and fast charge 

infrastructure are the major drivers (as opposed 

to electricity, home charge points, and other 

general and administrative costs). The 

combination of low battery cost, reduced vehicle 

range, and high swapping infrastructure costs 

can, however, elevate the cost of fast charge 

infrastructure over that of batteries themselves. It 

should further be noted that the high level of 

capital expenditures involved in the service 

provider’s business model also makes the cost of 

financing a very powerful variable.

In applying the calculated service fee to 

individual driver economics where the cost of 

unachievable VMT is high, our simulations show 

that fast charge service plans can be a more costeffective approach to electrifying travel for a 

significant number of drive patterns than direct 

ownership of a BEV75 under a broad range of 

scenarios for the costs of batteries and 

infrastructure. However, under our assumed cost of 

gasoline, tax structure, and absence of purchase 

incentives, we find that the TCO of the BEV 

service plan option is rarely more cost-effective 

than direct ownership of a CV. Only when battery 

costs reach the DOE’s most aggressive target 

($125/kWh) and infrastructure costs achieve our 

lowest assumed values do we see significant 

numbers of drive patterns benefiting economically 

over the CV option.

It should be noted that these results do not quantify 

the full potential of a service provider to improve 

the relative value of BEVs. Indeed, the economics 

of a service provider are yet to be fully optimized. 

For example, multi-tiered fee and service 

strategies, optimal allocation and down-cycling of 

aged batteries, revenue generation via aggregated 

vehicle-to-grid services, and other avenues that 

could further improve individual driver economics 

are yet to be explored. Moreover, it may be 

necessary to perform a detailed study of the 

geographic and temporal distribution of range 

extension events, which could significantly affect 

the total cost of infrastructure and subscribers’ 

utility factors.

Acknowledgments

The authors would like to acknowledge the DOE 

Vehicle Technologies Office managers David 

Howell and Brian Cunningham for their 

sponsorship of this study, as well as Kandler 

Smith, Aaron Brooker, and Eric Wood for their 

technical contributions.

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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Authors

Dr. Jeremy Neubauer is a Senior 

Engineer with NREL’s for 

Transportation and Hydrogen Systems

Center, where his research activities 

focus on lifetime analysis of batteries 

and optimizing battery use strategies 

for automotive and grid applications. 

Dr. Neubauer has a Bachelors, 

Masters, and Doctorate in Mechanical 

Engineering from Washington 

University in St. Louis.

Dr. Ahmad Pesaran is the Energy 

Storage Group Manager with NREL’s 

Transportation and Hydrogen Systems

Center. His team works on battery 

thermal characterization and testing, 

battery electrochemical-thermal 

analysis and modelling, developing 

high energy anodes, and battery and 

ultracapacitor simulations modelling 

and techno-economic analysis of

electric drive vehicles.